A Control Function Approach to Estimating Dynamic Probit Models with Endogenous Regressors, with an Application to the Study of Poverty Persistence in China

41 Pages Posted: 20 Apr 2016

See all articles by John Giles

John Giles

World Bank; IZA Institute of Labor Economics; World Bank - Development Research Group (DECRG)

Irina Murtazashvili

affiliation not provided to SSRN

Multiple version iconThere are 2 versions of this paper

Date Written: August 1, 2010

Abstract

This paper proposes a parametric approach to estimating a dynamic binary response panel data model that allows for endogenous contemporaneous regressors. This approach is of particular value for settings in which one wants to estimate the effects of an endogenous treatment on a binary outcome. The model is next used to examine the impact of rural-urban migration on the likelihood that households in rural China fall below the poverty line. In this application, it is shown that migration is important for reducing the likelihood that poor households remain in poverty and that non-poor households fall into poverty. Furthermore, it is demonstrated that failure to control for unobserved heterogeneity would lead the researcher to underestimate the impact of migrant labor markets on reducing the probability of falling into poverty.

Keywords: Rural Poverty Reduction, Population Policies, Achieving Shared Growth, Debt Markets, Regional Economic Development

Suggested Citation

Giles, John and Murtazashvili, Irina, A Control Function Approach to Estimating Dynamic Probit Models with Endogenous Regressors, with an Application to the Study of Poverty Persistence in China (August 1, 2010). World Bank Policy Research Working Paper No. 5400, Available at SSRN: https://ssrn.com/abstract=1658775

John Giles (Contact Author)

World Bank ( email )

Washington DC
United States

IZA Institute of Labor Economics

Schaumburg-Lippe-Str. 7 / 9
Bonn, D-53072
Germany

World Bank - Development Research Group (DECRG)

1818 H. Street, N.W.
MSN3-311
Washington, DC 20433
United States

Irina Murtazashvili

affiliation not provided to SSRN

No Address Available

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